Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
The janitor package contains only a little number of functions but nevertheless
it is surprisingly convenient.
I never really fully appreciated its functionality until I took a look into the documentation.
Of course, other packages can achieve the same thing too but janitor makes
a lot of tasks easy.
Thus, here is a little showcase.
Clean column names
As everyone working with data knows, data sets rarely come in a clean format. Often, the necessary cleaning process already starts with the column names. Here, take this data set from TidyTuesday, week 41.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 |
nurses <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-05/nurses.csv')
names(nurses)
## [1] "State"
## [2] "Year"
## [3] "Total Employed RN"
## [4] "Employed Standard Error (%)"
## [5] "Hourly Wage Avg"
## [6] "Hourly Wage Median"
## [7] "Annual Salary Avg"
## [8] "Annual Salary Median"
## [9] "Wage/Salary standard error (%)"
## [10] "Hourly 10th Percentile"
## [11] "Hourly 25th Percentile"
## [12] "Hourly 75th Percentile"
## [13] "Hourly 90th Percentile"
## [14] "Annual 10th Percentile"
## [15] "Annual 25th Percentile"
## [16] "Annual 75th Percentile"
## [17] "Annual 90th Percentile"
## [18] "Location Quotient"
## [19] "Total Employed (National)_Aggregate"
## [20] "Total Employed (Healthcare, National)_Aggregate"
## [21] "Total Employed (Healthcare, State)_Aggregate"
## [22] "Yearly Total Employed (State)_Aggregate"
|
These column names are intuitively easy to understand but not necessarily easy to process
by code as there are white spaces and other special characters.
Therefore, I accompany most data input by clean_names() from the janitor package.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 |
library(janitor) library(dplyr) # load for pipe %>% and later wrangling names(nurses %>% clean_names) ## [1] "state" ## [2] "year" ## [3] "total_employed_rn" ## [4] "employed_standard_error_percent" ## [5] "hourly_wage_avg" ## [6] "hourly_wage_median" ## [7] "annual_salary_avg" ## [8] "annual_salary_median" ## [9] "wage_salary_standard_error_percent" ## [10] "hourly_10th_percentile" ## [11] "hourly_25th_percentile" ## [12] "hourly_75th_percentile" ## [13] "hourly_90th_percentile" ## [14] "annual_10th_percentile" ## [15] "annual_25th_percentile" ## [16] "annual_75th_percentile" ## [17] "annual_90th_percentile" ## [18] "location_quotient" ## [19] "total_employed_national_aggregate" ## [20] "total_employed_healthcare_national_aggregate" ## [21] "total_employed_healthcare_state_aggregate" ## [22] "yearly_total_employed_state_aggregate" |
Did you see what happened?
White spaces were converted to _ and parantheses were removed.
Even the % signs were converted to percent.
Now, these labels are easy to understand AND process by code.
This does not mean that you are finished cleaning but at least now the columns
are more accessible.
Remove empty and or constant columns and rows
Data sets come with empty or superfluous rows or columns are not a rare sighting. This is especially true if you work with Excel files because there will be a lot of empty cells. Take a look at the dirty Excel data set from janitor’s GitHub page. It looks like this when you open it with Excel.
Taking a look just at this picture we may notice a couple of things.
-
First, Jason Bourne is teaching at a school. I guess being a trained assassin qualifies him to teach physical education. Also – and this is just a hunch – undercover work likely earned him his “Theater” certification.
-
Second, the header above the actual table will be annoying, so we must skip the first line when we read the data set.
-
Third, the column names are not ideal but we know how to deal with that by now.
-
Fourth, there are empty rows and columns we can get rid of.
-
Fifth, there is a column that contains only ‘YES’. Therefore it contains no information at all and can be removed.
So, let us read and clean the data.
The janitor package will help us with remove_empty() and remove_constant().
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 |
xl_file <- readxl::read_excel('dirty_data.xlsx', skip = 1) %>%
clean_names() %>%
remove_empty() %>%
remove_constant()
xl_file
## # A tibble: 12 x 9
## first_name last_name employee_status subject hire_date percent_allocated
## <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Jason Bourne Teacher PE 39690 0.75
## 2 Jason Bourne Teacher Drafting 43479 0.25
## 3 Alicia Keys Teacher Music 37118 1
## 4 Ada Lovelace Teacher <NA> 38572 1
## 5 Desus Nice Administration Dean 42791 1
## 6 Chien-Shiung Wu Teacher Physics 11037 0.5
## 7 Chien-Shiung Wu Teacher Chemistry 11037 0.5
## 8 James Joyce Teacher English 36423 0.5
## 9 Hedy Lamarr Teacher Science 27919 0.5
## 10 Carlos Boozer Coach Basketball 42221 NA
## 11 Young Boozer Coach <NA> 34700 NA
## 12 Micheal Larsen Teacher English 40071 0.8
## # ... with 3 more variables: full_time <chr>, certification_9 <chr>,
## # certification_10 <chr>
|
Here, remove_empty() defaulted to remove, both, rows and colums.
If we wish, we can change that by setting e.g. which = 'rows'.
Now, we may also want to see the hire_data in a sensible format.
For example, in this dirty data set, Jason Bourne was hired on 39690.
Luckily, our janitor can make sense of it all.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 |
xl_file %>% mutate(hire_date = excel_numeric_to_date(hire_date)) ## # A tibble: 12 x 9 ## first_name last_name employee_status subject hire_date percent_allocat~ ## <chr> <chr> <chr> <chr> <date> <dbl> ## 1 Jason Bourne Teacher PE 2008-08-30 0.75 ## 2 Jason Bourne Teacher Drafting 2019-01-14 0.25 ## 3 Alicia Keys Teacher Music 2001-08-15 1 ## 4 Ada Lovelace Teacher <NA> 2005-08-08 1 ## 5 Desus Nice Administration Dean 2017-02-25 1 ## 6 Chien-Shiung Wu Teacher Physics 1930-03-20 0.5 ## 7 Chien-Shiung Wu Teacher Chemistry 1930-03-20 0.5 ## 8 James Joyce Teacher English 1999-09-20 0.5 ## 9 Hedy Lamarr Teacher Science 1976-06-08 0.5 ## 10 Carlos Boozer Coach Basketball 2015-08-05 NA ## 11 Young Boozer Coach <NA> 1995-01-01 NA ## 12 Micheal Larsen Teacher English 2009-09-15 0.8 ## # ... with 3 more variables: full_time <chr>, certification_9 <chr>, ## # certification_10 <chr> |
Rounding
To my surprise shock, R uses some unexpected rounding rule.
In my world, whenever a number ends in .5, standard rounding would round up.
Apparently, R uses something called banker’s rounding that in these cases
rounds towards the next even number.
Take a look.
1 2 |
round(seq(0.5, 4.5, 1)) ## [1] 0 2 2 4 4 |
I would expect that the rounded vector contains the integers from one to five.
Thankfully, janitor offers a convenient rounding function.
1 2 |
round_half_up(seq(0.5, 4.5, 1)) ## [1] 1 2 3 4 5 |
Ok, so that gives us a new function for rounding towards integers.
But what is really convenient is that janitor can round_to_fractions.
1 2 |
round_to_fraction(seq(0.5, 2.0, 0.13), denominator = 4) ## [1] 0.50 0.75 0.75 1.00 1.00 1.25 1.25 1.50 1.50 1.75 1.75 2.00 |
Here, I rounded the numbers to the next quarters (denominator = 4) but of course
any fraction is possible.
You can now live the dream of rounding towards arbitrary fractions.
Find matches in multiple characteristics
In my opinion, the get_dupes() function is really powerful.
It allows us to find “similar” observations in a data set based on certain characteristics.
For example, the starwars data set from dplyr contains a lot of information
on characters from the Star Wars movies.
Possibly, we want to find out which characters are similar w.r.t. to certain traits.
1 2 3 4 5 6 7 8 9 10 11 12 13 |
starwars %>% get_dupes(eye_color, hair_color, skin_color, sex, homeworld) %>% select(1:8) ## # A tibble: 7 x 8 ## eye_color hair_color skin_color sex homeworld dupe_count name height ## <chr> <chr> <chr> <chr> <chr> <int> <chr> <int> ## 1 blue black yellow female Mirial 2 Luminara U~ 170 ## 2 blue black yellow female Mirial 2 Barriss Of~ 166 ## 3 blue blond fair male Tatooine 2 Luke Skywa~ 172 ## 4 blue blond fair male Tatooine 2 Anakin Sky~ 188 ## 5 brown brown light female Naboo 3 Cordé 157 ## 6 brown brown light female Naboo 3 Dormé 165 ## 7 brown brown light female Naboo 3 Padmé Amid~ 165 |
So, Luke and Anakin Skywalker are similar to one another.
Who would have thought that.
Sadly, I don’t enough about Star Wars to know whether the other matches are similarly
“surprising”.
In any case, the point here is that we can easily find matches according to
arbitrarily many characteristics.
Conveniently, these characteristics are the first columns of the new output and
we get a dupe_count.
Alright, this concludes our little showcase.
In the janitor package, there is another set of tabyl() functions.
These are meant to improve base R’s table() functions.
Since I rarely use that function I did not include it but if you use table() frequently,
then you should definitely check out tabyl().
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.